What is the purpose of np.array() in Python?

np.array() is a function in the NumPy library that is used to create array objects. This function converts input data (such as lists, tuples, arrays, etc.) into NumPy arrays. Its specific functions include: 1. Creating one-dimensional or multi-dimensional arrays: it can convert data like lists or tuples into NumPy arrays, allowing for the use of various array operations and methods provided by the NumPy library. 2. Converting data types: by specifying the dtype parameter, it can forcibly convert the input data’s data type, for example converting an integer list into a float array. 3. Copying arrays: arrays created with np.array() are new objects that do not share memory with the original data, so even if the original data changes, the created array will not be affected. 4. Quickly creating specific types of arrays: np.array() provides some functions for quickly creating specific types of arrays, such as np.zeros() for creating arrays of all zeros, np.ones() for creating arrays of all ones, np.random.rand() for creating arrays of random numbers, etc. In summary, np.array() is a very commonly used function for creating NumPy arrays, providing many related parameters and features for convenient array operations and processing.